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knitr::include_graphics("preview.jpg")
In this short article I am exploring what the Dutch political landscape would look like if it operated under a first-past-the-post (FPTP) system.
The election data for the Netherlands was downloaded from the Kiesraad. The shapefile was downloaded from ESRI NL.
Results Tweede Kamer Verkiezing 2021
These are the 2021 results summarised:
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#sort in long format
df_results[is.na(df_results)] <- 0
df_pr <- df_results %>%
pivot_longer(cols = VVD:Modern.Nederland, names_to = "party", values_to = "votes") %>%
select(party, votes) %>%
group_by(party) %>%
summarise(votes=sum(votes))
df_pr$party <- gsub("Partij.van.de.Arbeid..P.v.d.A..","PvdA",df_pr$party)
df_pr$party <- gsub("Democraten.66..D66.","D66",df_pr$party)
df_pr$party <- gsub("X50PLUS","Partij 50PLUS",df_pr$party)
df_pr$party <- gsub("Forum.voor.Democratie","FvD",df_pr$party)
df_pr$party <- gsub("Forum.voor.Democratie","FvD",df_pr$party)
df_pr$party <- gsub("SP..Socialistische.Partij.","SP",df_pr$party)
df_pr$party <- gsub("PVV..Partij.voor.de.Vrijheid.","PVV",df_pr$party)
df_pr$party <- gsub("Staatkundig.Gereformeerde.Partij..SGP.","SGP",df_pr$party)
df_pr$party <- gsub("Partij.voor.de.Dieren","PvdD",df_pr$party)
dHondt <- function(votes, parties, n_seats = 150) {
divisor.mat <- sum(votes) / sapply(votes, "/", seq(1, n_seats, 1))
colnames(divisor.mat) <- parties
m.mat <- tidyr::gather(as.data.frame(divisor.mat), key="name", value="value",
everything())
m.mat <- m.mat[rank(m.mat$value, ties.method = "random") <= n_seats, ]
rle.seats <- rle(as.character(m.mat$name))
if (sum(rle.seats$length) != n_seats)
stop(paste("Number of seats distributed not equal to", n_seats))
# fill up the vector with parties that got no seats
if (any(!(parties %in% rle.seats$values))) {
# add parties
missing_parties <- parties[!(parties %in% rle.seats$values)]
for (party in missing_parties) {
rle.seats$lengths <- c(rle.seats$lengths, 0)
rle.seats$values <- c(rle.seats$values, party)
}
# sort results
rle.seats$lengths <- rle.seats$lengths[match(parties, rle.seats$values)]
rle.seats$values <- rle.seats$values[match(parties, rle.seats$values)]
}
rle.seats$length
}
df_pr$seats<- dHondt(df_pr$votes,df_pr$party,150)
df_pr <- df_pr %>%
filter(seats>0) %>%
as.data.frame()
df_pr <- df_pr[order(df_pr$votes,decreasing =T),]
df_pr <- left_join(df_pr,colourscheme)
df_pr$total <- sum(df_pr$votes)
df_pr$percentage <- (df_pr$votes/df_pr$total)*100
df_pr$percentage <- round(df_pr$percentage,1)
resultaat <- df_pr %>%
select(party,votes,seats,percentage)
kable(resultaat)
| party | votes | seats | percentage |
|---|---|---|---|
| VVD | 2279126 | 34 | 22.3 |
| D66 | 1565862 | 24 | 15.3 |
| PVV | 1124482 | 17 | 11.0 |
| CDA | 990601 | 15 | 9.7 |
| SP | 623371 | 9 | 6.1 |
| PvdA | 597192 | 9 | 5.8 |
| GROENLINKS | 537308 | 8 | 5.3 |
| FvD | 523083 | 8 | 5.1 |
| PvdD | 399751 | 6 | 3.9 |
| ChristenUnie | 351275 | 5 | 3.4 |
| Volt | 252480 | 3 | 2.5 |
| JA21 | 246620 | 3 | 2.4 |
| SGP | 215249 | 3 | 2.1 |
| DENK | 211238 | 3 | 2.1 |
| Partij 50PLUS | 106702 | 1 | 1.0 |
| BBB | 104319 | 1 | 1.0 |
| BIJ1 | 87238 | 1 | 0.9 |
Visualising parliament using ggpol
Let’s start with examining the composition of the Tweede Kamer - under a proportional representation (D’Hondt) system. This is what the Tweede Kamer looks like currently:
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#plot proportional table with ggpol's geom_parliament.
ggplot(df_pr) +
ggpol::geom_parliament(aes(seats = seats, fill = party),color="black") +
#highlight the party in control of the House with a black line
scale_fill_manual(values = df_pr$colour,
labels = df_pr$party)+
coord_fixed()+
theme_void()
Now let’s have a look at what it would look like under First-past-the-Post (FPTP), with one MP elected per Gemeente.
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#sort in long format
df_long <- df_results %>%
pivot_longer(cols = VVD:Modern.Nederland, names_to = "party", values_to = "votes") %>%
select(regio =RegioNaam, code = RegioCode,party, votes) %>%
filter(!is.na(votes)) %>%
group_by(code) %>%
mutate(total = sum(votes)) %>%
slice_max(votes)
#convert to percentage, round and remove the G from the codes to match up
df_long$percentage <- (df_long$votes/df_long$total)*100
df_long$percentage <- round(df_long$percentage,1)
df_long$code <- gsub("G","",df_long$code)
#fix the names to acronyms for consistency
df_long$party <- gsub("Partij.van.de.Arbeid..P.v.d.A..","PvdA",df_long$party)
df_long$party <- gsub("Democraten.66..D66.","D66",df_long$party)
df_long$party <- gsub("X50PLUS","Partij 50PLUS",df_long$party)
df_long$party <- gsub("Forum.voor.Democratie","FvD",df_long$party)
df_long$party <- gsub("Forum.voor.Democratie","FvD",df_long$party)
df_long$party <- gsub("SP..Socialistische.Partij.","SP",df_long$party)
df_long$party <- gsub("PVV..Partij.voor.de.Vrijheid.","PVV",df_long$party)
df_long$party <- gsub("Staatkundig.Gereformeerde.Partij..SGP.","SGP",df_long$party)
df_long$party <- gsub("Partij.voor.de.Dieren","PvdD",df_long$party)
df_long$party <- as.factor(df_long$party)
#tidy up region names to exclude -'s and 's
df_long$regio <- gsub("'","",df_long$regio)
df_long$regio <- gsub("-"," ",df_long$regio)
#join names with corresponding colour scheme
df_long <- left_join(df_long,colourscheme)
#Tabulate the results in order to get a seat tally by party
fptp_results <- table(df_long$party) %>% as.data.frame()
colnames(fptp_results) <- c("party","seats")
fptp_results <- left_join(fptp_results,colourscheme)
#use geom_parliament from ggpol to visualise
ggplot(fptp_results) +
ggpol::geom_parliament(aes(seats = seats, fill = party),color="black") +
#highlight the party in control of the House with a black line
scale_fill_manual(values = fptp_results$colour,
labels = fptp_results$party)+
coord_fixed()+
theme_void()
Now, there are a couple of obvious differences:
- The VVD has an absolute majority, despite only having 22.3% of the vote.
- There are a lot more seats in this fictional house.
But also some obvious caveats:
- Each Gemeente has a different population, but here Amsterdam and Appingedam are represented equally. In the UK, with its FPTP system, constituencies are somewhat based on population. For example London has 73 MPs, while Scotland has 59 MPs.
Election results on a map
We can also show these results on a map - this in an interactive map which allows you to explore the individual election results for each Gemeente.
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#Prepare data for results per gemeente
#sort in long format
df_pie <- df_results %>%
pivot_longer(cols = VVD:Modern.Nederland, names_to = "party", values_to = "votes") %>%
select(regio =RegioNaam, code = RegioCode,party, votes) %>%
filter(!is.na(votes)) %>%
group_by(code) %>%
mutate(total = sum(votes))
df_pie$code <- gsub("G","",df_pie$code)
#bit of code to automatically remove the gemeentes that can be found in the results, but not on the map.
#these are the gemeentes Bonaire, Saba and Sint Eustasius which are in the Caribbean.
names_df <- unique(df_pie$code)
names_shp <- unique(shp_gemeentes$Gemeenteco)
names_remove <- names_df[!names_df %in% names_shp]
#remove Bonaire, Saba, Sint Eustasius
df_pie <- df_pie %>%
filter(!code %in% names_remove)
#round percentages
df_pie$percentage <- (df_pie$votes/df_pie$total)*100
df_pie$percentage <- round(df_pie$percentage,1)
#change name acronyms
#this step is being repeated, initially seperate script - move up to first chunk?
df_pie$party <- gsub("Partij.van.de.Arbeid..P.v.d.A..","PvdA",df_pie$party)
df_pie$party <- gsub("Democraten.66..D66.","D66",df_pie$party)
df_pie$party <- gsub("X50PLUS","Partij 50PLUS",df_pie$party)
df_pie$party <- gsub("Forum.voor.Democratie","FvD",df_pie$party)
df_pie$party <- gsub("Forum.voor.Democratie","FvD",df_pie$party)
df_pie$party <- gsub("SP..Socialistische.Partij.","SP",df_pie$party)
df_pie$party <- gsub("PVV..Partij.voor.de.Vrijheid.","PVV",df_pie$party)
df_pie$party <- gsub("Staatkundig.Gereformeerde.Partij..SGP.","SGP",df_pie$party)
df_pie$party <- gsub("Partij.voor.de.Dieren","PvdD",df_pie$party)
df_pie$party <- as.factor(df_pie$party)
df_pie$party <- as.character(df_pie$party)
df_pie$percentage <- as.numeric(df_pie$percentage)
#make a list of pie chart plots, in the order of code - this way it will correspond with the rows in the shapefile.
pie_plot_list <- lapply(unique(df_pie$code), function(i) {
#filter and group info per gemeente code
regio_df <- df_pie %>%
filter(code == i) %>%
dplyr::ungroup() %>%
select(party,percentage,regio) %>%
filter(percentage >0.1) %>%
as.data.frame()
#order by the vote tally
regio_df <- regio_df[order(regio_df$percentage,decreasing = T),]
#rejoin with the colourscheme
regio_df <- left_join(regio_df,colourscheme)
#plot as a bar chart, important to paste the corresponding gemeente name
ggplot(regio_df) +
geom_bar(aes(x = "", y = percentage, fill = party),
stat = "identity", colour = "black", width =1) +
#geom_text(aes(x = "", y = pct, label = percent(pct)), position = position_stack(vjust = 0.5))+
coord_polar("y", start = 0) +
labs(x = NULL, y = NULL, fill = NULL,
title = paste("Verkiezingsuitslag",unique(regio_df$regio))) +
theme_classic()+
scale_fill_manual(values = regio_df$colour,
limits = regio_df$party)+
theme(axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5, color = "black"),
legend.position = "bottom")
})
#remove saba, st eustasius etc.
df_long <- df_long %>%
filter(!code %in% names_remove)
#link to spatial
shp_fptp <- left_join(shp_gemeentes, df_long, by = c("Gemeenteco"="code"))
shp_fptp <- shp_fptp %>%
filter(percentage >0.1)
#I need to make the shapefile alphabetical to match with the pie chart list
#This means removing apostrophe's at 's-Gravenhage and 's-Hertogenbosch
shp_fptp$Gemeentena <- gsub("'","",shp_gemeentes$Gemeentena)
#reorder
shp_fptp <- shp_fptp[order(shp_fptp$Gemeentena),]
#reset rownumbers so they line up
rownames(shp_fptp) <- 1:nrow(shp_fptp)
#use mapview to display colour by party, add popupgrah which refers to the list of pie charts made earlier.
mapview(shp_fptp,
zcol = "party",
col.regions = shp_fptp$colour,
popup = popupGraph(pie_plot_list, width = 450,height =300))
Clicking on each gemeente will display a pie chart with its individual results. Some geographical patterns to note is that the Christian parties (SGP, CU) tend to do well in the so-called “bible-belt”. More Liberal and Green parties (D66, GroenLinks) perform well in the big cities. In rural areas where agriculture is an important industry, the CDA is a popular choice. The populist right-wing party, the PVV, does well on the fringes of the Netherlands - far from the centre of power where trust in politics is low.